The hierarchical model presented here builds on the previously proposed Hierarchical Predictive Belief Update (HPBU) [1]}.
HPBU was modeled after the assumed cortical micro-circuitry for predictive coding [2]} and is based on principles of active inference. It combines empirical Bayesian updates within a layer and linear dynamic updates (in form of a Kalman filter) between layers of its processing hierarchy. HPBU was developed as a generative model that learns, predicts and produces handwritten digits in an unsupervised way using hierarchical abstractions spanning from low-level movements to motor schemas corresponding to digits. This was used as a sensorimotor basis for a model of self-other distinction [1]} and was later extended to the social domain with additional levels that form a simplified mentalizing system to perform belief coordination in multi-agent scenarios [4]}.
In the present work we employ HPBU in more complex collaboration tasks and extend it with a mechanism for on-the-fly coordination as described next.
